← BioTransfer GEO Dataset Finder
GEO series

Single-Cell RNA-Seq Study Showing the Effect of Healthy and Cencer Cells on HSCs

GSE305700 Homo sapiens (human) Single-cell RNA-seq 16 samples Submitted 2025/08/20 Platform GPL34284
At a glance

Single-cell RNA-seq · human · 16 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism
Homo sapiens (human)
Samples
16 samples: 4 × Gene expression of CD34 BM cells samples treated by cancers and primary tissue exosomes and one untreated sample, Well vs 4 × ADT expression of CD34 BM cells samples treated by cancers and primary tissue exosomes and one untreated sample, Well vs 4 × Gene expression of CD34 PB cells samples treated by cancers and primary tissue exosomes and one untreated sample, Well vs 4 × ADT expression of CD34 PB cells samples treated by cancers and primary tissue exosomes and one untreated sample, WellGroups are read from the sample names, not from the paper. Check the list below.
Data you can get
A processed table (series matrix) and supplementary files from NCBI; the raw sequencing reads from ENA. Which of these hold counts depends on the study, so check the file list.
Paper
Enhanced hematopoietic stem cell self-renewal and engraftment through human lung exosomal communicationMolecular therapy : the journal of the American Society of Gene Therapy · 2025
Summary (written by the submitters)
Hematopoietic stem cells (HSCs) are highly sensitive to signals from their surrounding microenvironment, yet the precise effects of healthy versus malignant cells on HSC behavior remain poorly understood. In this study, we applied single-cell RNA sequencing (scRNA-seq) to profile HSCs exposed to either exosomes from healthy primary cells or cancer-derived cells. Our analyses revealed distinct transcriptional programs induced by each condition.
Published in
Enhanced hematopoietic stem cell self-renewal and engraftment through human lung exosomal communication
Benam SR, Wang X, Maleknia S et al. · Molecular therapy : the journal of the American Society of Gene Therapy 2025 · PMID 40798884 · doi:10.1016/j.ymthe.2025.08.020
This dataset
Download · which file do I need?

Direct links to NCBI and ENA, no account and no request form. Pick by what you want to do: take the small processed table to look, the supplementary files to re-analyse, or the original raw data to start over.

1 · Just have a look

Processed table

The series matrix: one table of values per sample as the submitters processed it. The smallest download, and it opens in Excel or R. Start here to see what the data look like.

2 · Re-analyse it

Supplementary files

The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE305700_RAW.tar (it can run to tens of gigabytes).

3 · Start from the raw reads

Raw sequencing reads

The original FASTQ files, straight from the sequencer, held at ENA. Large, and you align them yourself, so only choose this if you need to re-process the study from scratch.

What the files usually look like: Single-cell studies usually ship their counts as 10x files (matrix.mtx, barcodes.tsv, features.tsv), an .h5 file, or an .rds / .h5ad object that Seurat or Scanpy can open. Look for those among the supplementary files.

Also filed as BioProject PRJNA1307482 and SRA study SRP609849. Searching any of these in the dataset finder brings you back here.

Study design
4 × Gene expression of CD34 BM cells samples treated by cancers and primary tissue exosomes and one untreated sample, Well vs 4 × ADT expression of CD34 BM cells samples treated by cancers and primary tissue exosomes and one untreated sample, Well vs 4 × Gene expression of CD34 PB cells samples treated by cancers and primary tissue exosomes and one untreated sample, Well vs 4 × ADT expression of CD34 PB cells samples treated by cancers and primary tissue exosomes and one untreated sample, Well

Supports a between-group comparison across 16 samples.

How we read this: 4 replicated groups read from 16 sample titles; they account for 16 of them. These groups are guessed from the sample names, not taken from the paper, so check them against the sample list below before relying on them.

Samples in this study
Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

Share this dataset

Metadata from NCBI GEO, cached and refreshed periodically — the NCBI page above is authoritative. Downloads link straight to NCBI/ENA; nothing is proxied through BioTransfer.